Abstract: A system and method to profile an application use and identify data used for application execution, map the identified data for application execution to a virtual memory associated with application execution, including execution beginning at specific times, states or stages of the application, and transmit the virtual memory to an end user wishing to demonstrate the application on an end user device. The end user device can emulate the application from any desired application start time, state or stage using data at the end user device identified by the virtual memory.

Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Abstract: A system and method to profile an application use and identify data used for application execution, map the identified data for application execution to a virtual memory associated with application execution, including execution beginning at specific times, states or stages of the application, and transmit the virtual memory to an end user wishing to demonstrate the application on an end user device. The end user device can emulate the application from any desired application start time, state or stage using data at the end user device identified by the virtual memory.

Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Abstract: A system and method for optimizing clusters included in an app store are disclosed. The method comprises generating, by one or more proposal servers, one or more cluster proposals, wherein each of the one or more proposal servers executes a proposal algorithm, processing, by a cluster server, the one or more cluster proposals to resolve any conflicts within and across the one or more proposal servers, assigning a priority value to each of the one or more cluster proposals based on a predicted impact of the respective cluster proposal in the app store, forwarding to a review portal a predetermined amount of prioritized cluster proposals for review and approval, and publishing approved prioritized clusters proposals on the app store.

Abstract: Systems and methods of structures reviews through auto-generated tags are provided that include providing, with a computing device having an input device and a display device, a user interface to receive a review for an object from a reviewer, selecting a set of tags from an object tag collection stored in a database communicatively coupled to the computing device according to the object and the reviewer, displaying, by the display device of the computing device, the selected set of tags on a display, receiving an input, by the input device, to remove one or more of the displayed tags, and storing, by a storage device, the remaining tags of the set of tags that are submitted according to the received input for the object.

Abstract: A system and method of annotating an application, including obtaining input signals associated with a target application, wherein the input signals are obtained from a plurality of sources, obtaining first annotation data from the obtained input signals, generating second annotation data in a machine-understandable form based on the first annotation data, and associating the second annotation data with the target application.

Abstract: A given set of videos are sequenced in an aesthetically pleasing manner using models learned from human curated playlists. Semantic features associated with each video in the curated playlists are identified and a first order Markov chain model is learned from curated playlists. In one method, a directed graph using the Markov model is induced, wherein sequencing is obtained by finding the shortest path through the directed graph. In another method a sampling based approach is implemented to produce paths on the digraph. Multiple samples are generated and the best scoring sample is returned as the output. In a third method, a relevance based random walk sampling algorithm is modified to produce a reordering of the playlist.

Abstract: A demographics analysis trains classifier models for predicting demographic attribute values of videos and users not already having known demographics. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of videos using video features such as demographics of video uploaders, textual metadata, and/or audiovisual content of videos. In one embodiment, the demographics analysis system trains classifier models for predicting demographics of users (e.g., anonymous users) using user features based on prior video viewing periods of users. For example, viewing-period based user features can include individual viewing period statistics such as total videos viewed. Further, the viewing-period based features can include distributions of values over the viewing period, such as distributions in demographic attribute values of video uploaders, and/or distributions of viewings over hours of the day, days of the week, and the like.

Abstract: A video classification score boosting method boosts classification scores for videos for increased accuracy. A target video is classified with a classifier, producing a classification score. Related video scores are determined using the classifier for sets of videos related to the target video. The sets of related videos may include co-browsed videos, co-commented videos, co-queried videos, and co-uploaded videos. The related video scores may be the mean or median classification score for the classified sets of related videos. Weighting coefficients associated with the classifier are retrieved and applied to the classification score and the related video scores. The weighting coefficients may be determined for the classifier by classifying sets of pre-classified videos with the classifier and determining the weighting coefficients which, when applied to the classification scores of the pre-classified videos, improves the accuracy of the classification scores.

Abstract: A multi-phase process first trains a machine learned rating classifier, and then uses the rating classifier to automatically rate videos in a selected category in a way which mimics human rating. Panels of human viewers rate videos in tuples, and these tuples along with human preference data distilled from the ratings are used to create a training set that is used to train the machine learned rating classifier. The rating classifier becomes capable of predicting human preferences with regards to videos in the selected category. Optionally a second machine learned classifier can be trained to automatically identify videos in the selected category for the panels of human viewers to rate. The output of the multi-phase process can be used to highlight content that is predicted to be higher quality.